AI/ML Engineer

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  Select appropriate algorithms and frameworks based on business use cases and performance requirements.
• Conduct data preprocessing, feature engineering, and exploratory data analysis.

Cloud Architecture & Integration

• Architect scalable AI/ML solutions using Azure Machine Learning, Azure Cognitive Services, Azure Databricks, AWS SageMaker, AWS Rekognition, AWS Comprehend, and related services.
• Integrate AI/ML pipelines with data lakes, data warehouses, and APIs.
• Ensure solutions adhere to cloud architecture best practices, security, and compliance standards.

MLOps & Automation

• Implement and maintain CI/CD pipelines for ML model training, testing, and deployment.
• Leverage Azure DevOps, GitHub Actions, AWS CodePipeline, and Terraform/CloudFormation for automation.
• Monitor model performance and retrain models as needed.

Collaboration & Stakeholder Engagement

• Work closely with data engineers, data scientists, DevOps engineers, and product managers to align AI/ML solutions with business goals.
• Present solution designs, performance metrics, and recommendations to technical and non-technical stakeholders.

Security, Compliance & Governance

• Implement data privacy and compliance measures (e.g., HIPAA, GDPR, SOC 2).
• Apply responsible AI principles for fairness, transparency, and explainability.

Required Skills & Qualifications:

• Bachelor’s or Master’s degree in Computer Science, Data Science, AI/ML, or a related field.
• 5+ years of experience in AI/ML solution development and deployment.
• 3+ years of hands-on experience in Azure and AWS AI/ML services.
• Proficiency in Python, R, and relevant ML libraries (TensorFlow, PyTorch, scikit-learn, Hugging Face Transformers).
• Strong understanding of cloud-native data storage, processing, and streaming (Azure Data Lake, AWS S3, Azure Synapse, AWS Redshift, Kinesis, Event Hubs).
• Experience with Docker, Kubernetes (AKS/EKS) for containerized ML workloads.
• Familiarity with big data frameworks (Spark, Databricks)